Relevant bibliographies by topics / Recommendation graph

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Recommendation graph'

Author: Grafiati
Published: 25 May 2024

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Recommendation graph.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Recommendation graph"

1

Jiang, Liwei, Guanghui Yan, Hao Luo, and Wenwen Chang. "Improved Collaborative Recommendation Model: Integrating Knowledge Embedding and Graph Contrastive Learning." Electronics 12, no. 20 (2023): 4238. http://dx.doi.org/10.3390/electronics12204238.

Full text
Abstract:
A recommendation algorithm combined with a knowledge graph enables auxiliary information on items to be obtained by using the knowledge graph to achieve better recommendations. However, the recommendation performance of existing methods relies heavily on the quality of the knowledge graph. Knowledge graphs often contain noise and irrelevant connections between items and entities in the real world. This knowledge graph sparsity and noise significantly amplifies the noise effects and hinders the accurate representation of user preferences. In response to these problems, an improved collaborative
APA, Harvard, Vancouver, ISO, and other styles
2

Chen, Fukun, Guisheng Yin, Yuxin Dong, Gesu Li, and Weiqi Zhang. "KHGCN: Knowledge-Enhanced Recommendation with Hierarchical Graph Capsule Network." Entropy 25, no. 4 (2023): 697. http://dx.doi.org/10.3390/e25040697.

Full text
Abstract:
Knowledge graphs as external information has become one of the mainstream directions of current recommendation systems. Various knowledge-graph-representation methods have been proposed to promote the development of knowledge graphs in related fields. Knowledge-graph-embedding methods can learn entity information and complex relationships between the entities in knowledge graphs. Furthermore, recently proposed graph neural networks can learn higher-order representations of entities and relationships in knowledge graphs. Therefore, the complete presentation in the knowledge graph enriches the i
APA, Harvard, Vancouver, ISO, and other styles
3

Tolety, Venkata Bhanu Prasad, and Evani Venkateswara Prasad. "Graph Neural Networks for E-Learning Recommendation Systems." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 9s (2023): 43–50. http://dx.doi.org/10.17762/ijritcc.v11i9s.7395.

Full text
Abstract:
This paper presents a novel recommendation system for e-learning platforms. Recent years have seen the emergence of graph neural networks (GNNs) for learning representations over graph-structured data. Due to their promising performance in semi-supervised learning over graphs and in recommendation systems, we employ them in e-learning platforms for user profiling and content profiling. Affinity graphs between users and learning resources are constructed in this study, and GNNs are employed to generate recommendations over these affinity graphs. In the context of e-learning, our proposed approa
APA, Harvard, Vancouver, ISO, and other styles
4

Wang, Yan, Zhixuan Chu, Xin Ouyang, et al. "LLMRG: Improving Recommendations through Large Language Model Reasoning Graphs." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 17 (2024): 19189–96. http://dx.doi.org/10.1609/aaai.v38i17.29887.

Full text
Abstract:
Recommendation systems aim to provide users with relevant suggestions, but often lack interpretability and fail to capture higher-level semantic relationships between user behaviors and profiles. In this paper, we propose a novel approach that leverages large language models (LLMs) to construct personalized reasoning graphs. These graphs link a user's profile and behavioral sequences through causal and logical inferences, representing the user's interests in an interpretable way. Our approach, LLM reasoning graphs (LLMRG), has four components: chained graph reasoning, divergent extension, self
APA, Harvard, Vancouver, ISO, and other styles
5

Liu, Jiawei, Haihan Gao, Chuan Shi, Hongtao Cheng, and Qianlong Xie. "Self-Supervised Spatio-Temporal Graph Learning for Point-of-Interest Recommendation." Applied Sciences 13, no. 15 (2023): 8885. http://dx.doi.org/10.3390/app13158885.

Full text
Abstract:
As one of the most crucial topics in the recommendation system field, point-of-interest (POI) recommendation aims to recommending potential interesting POIs to users. Recently, graph neural networks have been successfully used to model interaction and spatio-temporal information in POI recommendations, but the data sparsity of POI recommendations affects the training of GNNs. Although some existing GNN-based POI recommendation approaches try to use social relationships or user attributes to alleviate the data sparsity problem, such auxiliary information is not always available for privacy reas
APA, Harvard, Vancouver, ISO, and other styles
6

Li, Ran, Yuexin Li, Jingsheng Lei, and Shengying Yang. "A Multi-Behavior Recommendation Method for Users Based on Graph Neural Networks." Applied Sciences 13, no. 16 (2023): 9315. http://dx.doi.org/10.3390/app13169315.

Full text
Abstract:
Most existing recommendation models only consider single user–item interaction information, which leads to serious cold-start or data sparsity problems. In practical applications, a user’s behavior is multi-type, and different types of user behavior show different semantic information. To achieve more accurate recommendations, a major challenge comes from being able to handle heterogeneous behavior data from users more finely. To address this problem, this paper proposes a multi-behavior recommendation framework based on a graph neural network, which captures personalized semantics of specific
APA, Harvard, Vancouver, ISO, and other styles
7

Wu, Ziteng, Chengyun Song, Yunqing Chen, and Lingxuan Li. "A review of recommendation system research based on bipartite graph." MATEC Web of Conferences 336 (2021): 05010. http://dx.doi.org/10.1051/matecconf/202133605010.

Full text
Abstract:
The interaction history between users and items is usually stored and displayed in the form of bipartite graphs. Neural network recommendation based on the user-item bipartite graph has a significant effect on alleviating the long-standing data sparseness and cold start of the recommendation system. The whole paper is based on the bipartite graph. An review of the recommendation system of graphs summarizes the three characteristics of graph neural network processing bipartite graph data in the recommendation field: interchangeability, Multi-hop transportability, and strong interpretability. Th
APA, Harvard, Vancouver, ISO, and other styles
8

Yu, Wenhui, Zixin Zhang, and Zheng Qin. "Low-Pass Graph Convolutional Network for Recommendation." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 8 (2022): 8954–61. http://dx.doi.org/10.1609/aaai.v36i8.20878.

Full text
Abstract:
Spectral graph convolution is extremely time-consuming for large graphs, thus existing Graph Convolutional Networks (GCNs) reconstruct the kernel by a polynomial, which is (almost) fixed. To extract features from the graph data by learning kernels, Low-pass Collaborative Filter Network (LCFN) was proposed as a new paradigm with trainable kernels. However, there are two demerits of LCFN: (1) The hypergraphs in LCFN are constructed by mining 2-hop connections of the user-item bipartite graph, thus 1-hop connections are not used, resulting in serious information loss. (2) LCFN follows the general
APA, Harvard, Vancouver, ISO, and other styles
9

Zhang, Shengzhe, Liyi Chen, Chao Wang, Shuangli Li, and Hui Xiong. "Temporal Graph Contrastive Learning for Sequential Recommendation." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 8 (2024): 9359–67. http://dx.doi.org/10.1609/aaai.v38i8.28789.

Full text
Abstract:
Sequential recommendation is a crucial task in understanding users' evolving interests and predicting their future behaviors. While existing approaches on sequence or graph modeling to learn interaction sequences of users have shown promising performance, how to effectively exploit temporal information and deal with the uncertainty noise in evolving user behaviors is still quite challenging. To this end, in this paper, we propose a Temporal Graph Contrastive Learning method for Sequential Recommendation (TGCL4SR) which leverages not only local interaction sequences but also global temporal gra
APA, Harvard, Vancouver, ISO, and other styles
10

Zeng, Yiping, and Shumin Liu. "Research on recommendation algorithm of Graph attention Network based on Knowledge graph." Journal of Physics: Conference Series 2113, no. 1 (2021): 012085. http://dx.doi.org/10.1088/1742-6596/2113/1/012085.

Full text
Abstract:
Abstract The introduction of knowledge graph as the auxiliary information of recommendation system provides a new research idea for personalized intelligent recommendation. However, most of the existing knowledge graph recommendation algorithms fail to effectively solve the problem of unrelated entities, leading to inaccurate prediction of potential preferences of users. To solve this problem, this paper proposes a KG-IGAT model combining knowledge graph and graph attention network, and adds an interest evolution module to graph attention network to capture user interest changes and generate t
APA, Harvard, Vancouver, ISO, and other styles

Dissertations / Theses on the topic "Recommendation graph"

1

Huang, Zan. "GRAPH-BASED ANALYSIS FOR E-COMMERCE RECOMMENDATION." Diss., Tucson, Arizona : University of Arizona, 2005. http://etd.library.arizona.edu/etd/GetFileServlet?file=file:///data1/pdf/etd/azu%5Fetd%5F1167%5F1%5Fm.pdf&type=application/pdf.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Larsson, Carl-Johan. "Movie Recommendation System Using Large Scale Graph-Processing." Thesis, KTH, Skolan för elektro- och systemteknik (EES), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-200601.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Söderkvist, Nils. "Recommendation system for job coaches." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-446792.

Full text
Abstract:
For any unemployed person in Sweden that is looking for a job, the most common place they can turn to is the Swedish Public Employment Service, also known as Arbetsförmedlingen, where they can register to get help with the job search process. Occasionally, in order to land an employment, the person might require extra guidance and education, Arbetsförmedlingen outsource this education to external companies called providers where each person gets assigned a coach that can assist them in achieving an employment quicker. Given the current labour market data, can the data be used to help optimize
APA, Harvard, Vancouver, ISO, and other styles
4

Ozturk, Gizem. "A Hybrid Veideo Recommendation System Based On A Graph Based Algorithm." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12612624/index.pdf.

Full text
Abstract:
This thesis proposes the design, development and evaluation of a hybrid video recommendation system. The proposed hybrid video recommendation system is based on a graph algorithm called Adsorption. Adsorption is a collaborative filtering algorithm in which relations between users are used to make recommendations. Adsorption is used to generate the base recommendation list. In order to overcome the problems that occur in pure collaborative system, content based filtering is injected. Content based filtering uses the idea of suggesting similar items that matches user preferences. In order to use
APA, Harvard, Vancouver, ISO, and other styles
5

Landia, Nikolas. "Content-awareness and graph-based ranking for tag recommendation in folksonomies." Thesis, University of Warwick, 2013. http://wrap.warwick.ac.uk/58069/.

Full text
Abstract:
Tag recommendation algorithms aid the social tagging process in many userdriven document indexing applications, such as social bookmarking and publication sharing websites. This thesis gives an overview of existing tag recommendation methods and proposes novel approaches that address the new document problem and the task of ranking tags. The focus is on graph-based methods such as Folk- Rank that apply weight spreading algorithms to a graph representation of the folksonomy. In order to suggest tags for previously untagged documents, extensions are presented that introduce content into the reco
APA, Harvard, Vancouver, ISO, and other styles
6

Priya, Rashmi. "RETAIL DATA ANALYTICS USING GRAPH DATABASE." UKnowledge, 2018. https://uknowledge.uky.edu/cs_etds/67.

Full text
Abstract:
Big data is an area focused on storing, processing and visualizing huge amount of data. Today data is growing faster than ever before. We need to find the right tools and applications and build an environment that can help us to obtain valuable insights from the data. Retail is one of the domains that collects huge amount of transaction data everyday. Retailers need to understand their customer’s purchasing pattern and behavior in order to take better business decisions. Market basket analysis is a field in data mining, that is focused on discovering patterns in retail’s transaction data. Our
APA, Harvard, Vancouver, ISO, and other styles
7

Olmucci, Poddubnyy Oleksandr. "Graph Neural Networks for Recommender Systems." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2022. http://amslaurea.unibo.it/25033/.

Full text
Abstract:
In recent years, a new type of deep learning models, Graph Neural Networks (GNNs), have demonstrated to be a powerful learning paradigm when applied to problems that can be described via graph data, due to their natural ability to integrate representations across nodes that are connected via some topological structure. One of such domains is Recommendation Systems, the majority of whose data can be naturally represented via graphs. For example, typical item recommendation datasets can be represented via user-item bipartite graphs, social recommendation datasets by social networks, and so on. T
APA, Harvard, Vancouver, ISO, and other styles
8

Bereczki, Márk. "Graph Neural Networks for Article Recommendation based on Implicit User Feedback and Content." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-300092.

Full text
Abstract:
Recommender systems are widely used in websites and applications to help users find relevant content based on their interests. Graph neural networks achieved state- of-the- art results in the field of recommender systems, working on data represented in the form of a graph. However, most graph- based solutions hold challenges regarding computational complexity or the ability to generalize to new users. Therefore, we propose a novel graph- based recommender system, by modifying Simple Graph Convolution, an approach for efficient graph node classification, and add the capability of generalizing t
APA, Harvard, Vancouver, ISO, and other styles
9

You, Di. "Attributed Multi-Relational Attention Network for Fact-checking URL Recommendation." Digital WPI, 2019. https://digitalcommons.wpi.edu/etd-theses/1321.

Full text
Abstract:
To combat fake news, researchers mostly focused on detecting fake news and journalists built and maintained fact-checking sites (e.g., Snopes.com and Politifact.com). However, fake news dissemination has been greatly promoted by social media sites, and these fact-checking sites have not been fully utilized. To overcome these problems and complement existing methods against fake news, in this thesis, we propose a deep-learning based fact-checking URL recommender system to mitigate impact of fake news in social media sites such as Twitter and Facebook. In particular, our proposed framework consi
APA, Harvard, Vancouver, ISO, and other styles
10

Lisena, Pasquale. "Knowledge-based music recommendation : models, algorithms and exploratory search." Electronic Thesis or Diss., Sorbonne université, 2019. http://www.theses.fr/2019SORUS614.

Full text
Abstract:
Représenter l'information décrivant la musique est une activité complexe, qui implique différentes sous-tâches. Ce manuscrit de thèse porte principalement sur la musique classique et étudie comment représenter et exploiter ses informations. L'objectif principal est l'étude de stratégies de représentation et de découverte des connaissances appliquées à la musique classique, dans des domaines tels que la production de base de connaissances, la prédiction de métadonnées et les systèmes de recommandation. Nous proposons une architecture pour la gestion des métadonnées de musique à l'aide des techn
APA, Harvard, Vancouver, ISO, and other styles

Books on the topic "Recommendation graph"

1

Varlamov, Oleg. Fundamentals of creating MIVAR expert systems. INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1513119.

Full text
Abstract:
Methodological and applied issues of the basics of creating knowledge bases and expert systems of logical artificial intelligence are considered. The software package "MIV Expert Systems Designer" (KESMI) Wi!Mi RAZUMATOR" (version 2.1), which is a convenient tool for the development of intelligent information systems. Examples of creating mivar expert systems and several laboratory works are given. The reader, having studied this tutorial, will be able to independently create expert systems based on KESMI. 
 The textbook in the field of training "Computer Science and Computer Engineering"
APA, Harvard, Vancouver, ISO, and other styles
2

Varlamov, Oleg. Mivar databases and rules. INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1508665.

Full text
Abstract:
The multidimensional open epistemological active network MOGAN is the basis for the transition to a qualitatively new level of creating logical artificial intelligence. Mivar databases and rules became the foundation for the creation of MOGAN. The results of the analysis and generalization of data representation structures of various data models are presented: from relational to "Entity — Relationship" (ER-model). On the basis of this generalization, a new model of data and rules is created: the mivar information space "Thing-Property-Relation". The logic-computational processing of data in th
APA, Harvard, Vancouver, ISO, and other styles
3

Levy, Barry S., ed. Social Injustice and Public Health. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780190914653.001.0001.

Full text
Abstract:
The third edition of Social Injustice and Public Health provides a comprehensive, up-to-date resource on the relationship of social injustice to the broad field of public health. It includes 29 chapters and many text boxes on a wide range of relevant issues written by 78 contributors who are expert in their respective areas of work. The book includes many descriptions of social injustice and its adverse effects on health, supplemented with many tables, graphs, photographs, and case examples—and many recommendations on what needs to be done to address social injustice. Social Injustice and Publ
APA, Harvard, Vancouver, ISO, and other styles

Book chapters on the topic "Recommendation graph"

1

Lodhi, Aminah Bilal, Muhammad Abdullah Bilal, Hafiz Syed Muhammad Bilal, et al. "PNRG: Knowledge Graph-Driven Methodology for Personalized Nutritional Recommendation Generation." In Digital Health Transformation, Smart Ageing, and Managing Disability. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43950-6_20.

Full text
Abstract:
AbstractChronic Diseases are a prevalent problem that affects millions of people worldwide. It is a prevalent health condition that requires careful diet and medication management and preventing chronic diseases. Traditional approaches to nutritional recommendation generation often rely on generic guidelines and population-based data, which may not account for individual dietary needs and preferences variations. In this paper, we propose a knowledge graph driven methodology for generating highly personalized nutritional recommendations that leverage the power of knowledge graphs to integrate and analyze complex data about an individual's health, lifestyle, and dietary habits. Our methodology employs a multi-step process that includes data collection and curation, knowledge graph construction, and personalized recommendation generation. We illustrate the effectiveness of our approach through a case study in which we generate personalized nutritional recommendations for a sample individual based on their specific health and dietary goals.
APA, Harvard, Vancouver, ISO, and other styles
2

Shi, Chuan, Xiao Wang, and Philip S. Yu. "Heterogeneous Graph Representation for Recommendation." In Artificial Intelligence: Foundations, Theory, and Algorithms. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-6166-2_7.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Zhang, Yuanyuan, Maosheng Sun, Xiaowei Zhang, and Yonglong Zhang. "Multi-task Feature Learning for Social Recommendation." In Knowledge Graph and Semantic Computing: Knowledge Graph Empowers New Infrastructure Construction. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-6471-7_18.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Xue, Feng, Wenjie Zhou, Zikun Hong, and Kang Liu. "Multi-stage Knowledge Propagation Network for Recommendation." In Knowledge Graph and Semantic Computing: Knowledge Graph Empowers New Infrastructure Construction. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-6471-7_19.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Tien, Dong Nguyen, and Hai Pham Van. "Graph Neural Network Combined Knowledge Graph for Recommendation System." In Computational Data and Social Networks. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-66046-8_6.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Guo, Zengqiang, Yan Yang, Jijie Zhang, Tianqi Zhou, and Bangyu Song. "Knowledge Graph Bidirectional Interaction Graph Convolutional Network for Recommendation." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-15931-2_44.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Chatterjee, Aniruddha, Sagnik Biswas, and M. Kanchana. "Patent Recommendation Engine Using Graph Database." In Computational Intelligence and Data Analytics. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-3391-2_36.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Liufu, Yuanwei, and Hong Shen. "Social Recommendation via Graph Attentive Aggregation." In Parallel and Distributed Computing, Applications and Technologies. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-96772-7_34.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Zhu, Jinghua, Yanchang Cui, Zhuohao Zhang, and Heran Xi. "Knowledge Graph Transformer for Sequential Recommendation." In Artificial Neural Networks and Machine Learning – ICANN 2023. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-44223-0_37.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Wen, Bo, Shumin Deng, and Huajun Chen. "Knowledge-Enhanced Collaborative Meta Learner for Long-Tail Recommendation." In Knowledge Graph and Semantic Computing: Knowledge Graph and Cognitive Intelligence. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1964-9_26.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Recommendation graph"

1

Gôlo, Marcos P. S., Leonardo G. Moraes, Rudinei Goularte, and Ricardo M. Marcacini. "One-Class Recommendation through Unsupervised Graph Neural Networks for Link Prediction." In Symposium on Knowledge Discovery, Mining and Learning. Sociedade Brasileira de Computação - SBC, 2022. http://dx.doi.org/10.5753/kdmile.2022.227810.

Full text
Abstract:
Recommender systems play a key role in every online platform to provide users a better experience. Many classic recommendation approaches might find issues, mainly modeling user relations. Graphs can naturally model these relations since we can connect users interacting with items. On the other hand, when we model user-item relations through graphs, we do not have interactions between all users and items. In addition, there are few non-recommendation interactions, which makes it challenging to cover this scope. Also, the scope of what will not be recommended for the user is greater than what w
APA, Harvard, Vancouver, ISO, and other styles
2

Tian, Yijun, Chuxu Zhang, Zhichun Guo, Chao Huang, Ronald Metoyer, and Nitesh V. Chawla. "RecipeRec: A Heterogeneous Graph Learning Model for Recipe Recommendation." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/481.

Full text
Abstract:
Recipe recommendation systems play an essential role in helping people decide what to eat. Existing recipe recommendation systems typically focused on content-based or collaborative filtering approaches, ignoring the higher-order collaborative signal such as relational structure information among users, recipes and food items. In this paper, we formalize the problem of recipe recommendation with graphs to incorporate the collaborative signal into recipe recommendation through graph modeling. In particular, we first present URI-Graph, a new and large-scale user-recipe-ingredient graph. We then
APA, Harvard, Vancouver, ISO, and other styles
3

Sang, Lei, and Lei Li. "Neural Collaborative Recommendation with Knowledge Graph." In 2020 IEEE International Conference on Knowledge Graph (ICKG). IEEE, 2020. http://dx.doi.org/10.1109/icbk50248.2020.00038.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Jin, Yuanyuan, Wei Zhang, Mingyou Sun, Xing Luo, and Xiaoling Wang. "Neural Restaurant-aware Dish Recommendation." In 2020 IEEE International Conference on Knowledge Graph (ICKG). IEEE, 2020. http://dx.doi.org/10.1109/icbk50248.2020.00090.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Cao, Bin, Jianwei Yin, Shuiguang Deng, Dongjing Wang, and Zhaohui Wu. "Graph-based workflow recommendation." In the 21st ACM international conference. ACM Press, 2012. http://dx.doi.org/10.1145/2396761.2398466.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Yang, Kaige, and Laura Toni. "GRAPH-BASED RECOMMENDATION SYSTEM." In 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP). IEEE, 2018. http://dx.doi.org/10.1109/globalsip.2018.8646359.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Li, Chaoliu, Lianghao Xia, Xubin Ren, Yaowen Ye, Yong Xu, and Chao Huang. "Graph Transformer for Recommendation." In SIGIR '23: The 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 2023. http://dx.doi.org/10.1145/3539618.3591723.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Xia, Lianghao, Yizhen Shao, Chao Huang, Yong Xu, Huance Xu, and Jian Pei. "Disentangled Graph Social Recommendation." In 2023 IEEE 39th International Conference on Data Engineering (ICDE). IEEE, 2023. http://dx.doi.org/10.1109/icde55515.2023.00180.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Dossena, Marco, Christopher Irwin, and Luigi Portinale. "Graph-based Recommendation using Graph Neural Networks." In 2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, 2022. http://dx.doi.org/10.1109/icmla55696.2022.00270.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Zhou, Chunyi, Yuanyuan Jin, Xiaoling Wang, and Yingjie Zhang. "Conversational Music Recommendation based on Bandits." In 2020 IEEE International Conference on Knowledge Graph (ICKG). IEEE, 2020. http://dx.doi.org/10.1109/icbk50248.2020.00016.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Reports on the topic "Recommendation graph"

1

Rinaudo, Christina, William Leonard, Jaylen Hopson, Christopher Morey, Robert Hilborn, and Theresa Coumbe. Enabling understanding of artificial intelligence (AI) agent wargaming decisions through visualizations. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/48418.

Full text
Abstract:
The process to develop options for military planning course of action (COA) development and analysis relies on human subject matter expertise. Analyzing COAs requires examining several factors and understanding complex interactions and dependencies associated with actions, reactions, proposed counteractions, and multiple reasonable outcomes. In Fiscal Year 2021, the Institute for Systems Engineering Research team completed efforts resulting in a wargaming maritime framework capable of training an artificial intelligence (AI) agent with deep reinforcement learning (DRL) techniques within a mari
APA, Harvard, Vancouver, ISO, and other styles
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography